The Mouse Genome Database (MGD): facilitating mouse as a model for human biology and disease

A systematic survey to identify lethal recessive variation in highly managed pig populations

BACKGROUND

Lethal recessive variation can cause prenatal death of homozygous offspring. Although usually present at low-frequency in populations, the impact on individual fitness can be substantial. Until recently, the presence of recessive embryonic lethal variation could only be measured indirectly through reduced fertility. In this study, we estimate the presence of genetic loci associated with both early and late termination of development during gestation in pigs from the wealth of genome data routinely generated by a commercial breeding company.

RESULTS

We examined three commercial pig (Sus scrofa) populations for potentially deleterious genetic variation based on 80 K SNP-chip genotypes, and estimate the effects on reproductive traits. 24,000 pigs from three populations were analyzed for missing or depletion of homozygous haplotypes. We identified 145 haplotypes (ranging from 0.5-4 Mb in size) in the genome with complete absence or depletion of homozygous animals. Thirty-five haplotypes show a negative effect on at least one of the analysed reproductive traits (total number born, number of stillborn, and number of mummified piglets). One variant in particular appeared to result in relative late termination of development of fetuses, responsible for a significant fraction of observed stillborn piglets ('mummies'), as they die mid-gestation. Moreover, we identified the BMPER gene as a likely candidate underlying this phenomenon.

CONCLUSIONS

Our study shows that although lethal recessive variation is present, the frequency of these alleles is invariably low in these highly managed populations. Nevertheless, due to cumulative effects of deleterious variants, large numbers of affected offspring are produced. Furthermore, our study demonstrates the use of a large-scale commercial genetic experiment to systematically screen for 'natural knockouts' that can increase understanding of gene function.

MicroRNA expression profiles and type 1 diabetes mellitus: systematic review and bioinformatic analysis

Growing evidence indicates that microRNAs (miRNAs) have a key role in processes involved in type 1 diabetes mellitus (T1DM) pathogenesis, including immune system functions and beta-cell metabolism and death. Although dysregulated miRNA profiles have been identified in T1DM patients, results are inconclusive; with only few miRNAs being consistently dysregulated among studies. Thus, we performed a systematic review of the literature on the subject, followed by bioinformatic analysis, to point out which miRNAs are dysregulated in T1DM-related tissues and in which pathways they act. PubMed and EMBASE were searched to identify all studies that compared miRNA expressions between T1DM patients and non-diabetic controls. Search was completed in August, 2017. Those miRNAs consistently dysregulated in T1DM-related tissues were submitted to bioinformatic analysis, using six databases of miRNA-target gene interactions to retrieve their putative targets and identify potentially affected pathways under their regulation. Thirty-three studies were included in the systematic review: 19 of them reported miRNA expressions in human samples, 13 in murine models and one in both human and murine samples. Among 278 dysregulated miRNAs reported in these studies, 25.9% were reported in at least 2 studies; however, only 48 of them were analyzed in tissues directly related to T1DM pathogenesis (serum/plasma, pancreas and peripheral blood mononuclear cells (PBMCs)). Regarding circulating miRNAs, 11 were consistently dysregulated in T1DM patients compared to controls: miR-21-5p, miR-24-3p, miR-100-5p, miR-146a-5p, miR-148a-3p, miR-150-5p, miR-181a-5p, miR-210-5p, miR-342-3p, miR-375 and miR-1275. The bioinformatic analysis retrieved a total of 5867 validated and 2979 predicted miRNA-target interactions for human miRNAs. In functional enrichment analysis of miRNA target genes, 77 KEGG terms were enriched for more than one miRNA. These miRNAs are involved in pathways related to immune system function, cell survival, cell proliferation and insulin biosynthesis and secretion. In conclusion, eleven circulating miRNAs seem to be dysregulated in T1DM patients in different studies, being potential circulating biomarkers of this disease.

Schwann Cells in the Ventral Dermis Do Not Derive from Myf5-Expressing Precursors

The embryonic origin of lineage precursors of the trunk dermis is somewhat controversial. Precursor cells traced by Myf5 and Twist2 (Dermo1) promoter activation (i.e., cells of presumed dermomyotomal lineage) have been reported to generate Schwann cells. On the other hand, abundant data demonstrate that dermal Schwann cells derive from the neural crest. This is relevant because dermal precursors give rise to neural lineages, and multilineage differentiation potential qualifies them as adult stem cells. However, it is currently unclear whether neural lineages arise from dedifferentiated Schwann cells instead of mesodermally derived dermal precursor cells. To clarify these discrepancies, we traced SOX2⁺ adult dermal precursor cells by two independent Myf5 lineage tracing strains. We demonstrate that dermal Schwann cells do not belong to the Myf5⁺ cell lineage, indicating that previous tracing data reflected aberrant cre recombinase expression and that bona fide Myf5⁺ dermal precursors cannot transdifferentiate to neural lineages in physiological conditions.

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Adult Myf5-cre^Sor mice aberrantly trace dermal Schwann cells (dSCs)

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Dedifferentiated, SOX2⁺ dSCs are the neural-competent precursors in the dermis

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These findings cast doubt on the multipotency of adult skin-derived precursors

Noncoding copy-number variations are associated with congenital limb malformation

PurposeCopy-number variants (CNVs) are generally interpreted by linking the effects of gene dosage with phenotypes. The clinical interpretation of noncoding CNVs remains challenging. We investigated the percentage of disease-associated CNVs in patients with congenital limb malformations that affect noncoding cis-regulatory sequences versus genes sensitive to gene dosage effects.MethodsWe applied high-resolution copy-number analysis to 340 unrelated individuals with isolated limb malformation. To investigate novel candidate CNVs, we re-engineered human CNVs in mice using clustered regularly interspaced short palindromic repeats (CRISPR)-based genome editing.ResultsOf the individuals studied, 10% harbored CNVs segregating with the phenotype in the affected families. We identified 31 CNVs previously associated with congenital limb malformations and four novel candidate CNVs. Most of the disease-associated CNVs (57%) affected the noncoding cis-regulatory genome, while only 43% included a known disease gene and were likely to result from gene dosage effects. In transgenic mice harboring four novel candidate CNVs, we observed altered gene expression in all cases, indicating that the CNVs had a regulatory effect either by changing the enhancer dosage or altering the topological associating domain architecture of the genome.ConclusionOur findings suggest that CNVs affecting noncoding regulatory elements are a major cause of congenital limb malformations.

Learning causal networks with latent variables from multivariate information in genomic data

Learning causal networks from large-scale genomic data remains challenging in absence of time series or controlled perturbation experiments. We report an information- theoretic method which learns a large class of causal or non-causal graphical models from purely observational data, while including the effects of unobserved latent variables, commonly found in many genomic datasets. Starting from a complete graph, the method iteratively removes dispensable edges, by uncovering significant information contributions from indirect paths, and assesses edge-specific confidences from randomization of available data. The remaining edges are then oriented based on the signature of causality in observational data. The approach and associated algorithm, miic, outperform earlier methods on a broad range of benchmark networks. Causal network reconstructions are presented at different biological size and time scales, from gene regulation in single cells to whole genome duplication in tumor development as well as long term evolution of vertebrates. Miic is publicly available at https://github.com/miicTeam/MIIC.

The reconstruction of causal networks from genomic data is an important but challenging problem. Predicting key regulatory interactions or genomic alterations at the origin of human diseases can guide experimental investigation and ultimately inspire innovative therapy. However, causal relationships are difficult to establish without the possibility to directly perturb the organisms’ genome for ethical or practical reasons. Besides, unmeasured (latent) variables may be hidden in many genomic datasets and lead to spurious causal relationships between observed variables. We propose in this paper an efficient computational approach, miic, that overcomes these limitations and learns causal networks from non-perturbative (observational) data in the presence of latent variables. In addition, we assess the confidence of each predicted interaction and demonstrate the enhanced robustness and accuracy of miic compared to alternative existing methods. This approach can be applied on a wide range of datasets and provide new biological insights on regulatory networks from single cell expression data or genomic alterations during tumor development. Miic is implemented in an R package freely available to the scientific community under a General Public License.

Mutation intolerant genes and targets of FMRP are enriched for nonsynonymous alleles in schizophrenia

Risk of schizophrenia is conferred by alleles occurring across the full spectrum of frequencies from common SNPs of weak effect through to ultra rare alleles, some of which may be moderately to highly penetrant. Previous studies have suggested that some of the risk of schizophrenia is attributable to uncommon alleles represented on Illumina exome arrays. Here, we present the largest study of exomic variation in schizophrenia to date, using samples from the United Kingdom and Sweden (10,011 schizophrenia cases and 13,791 controls). Single variants, genes, and gene sets were analyzed for association with schizophrenia. No single variant or gene reached genome-wide significance. Among candidate gene sets, we found significant enrichment for rare alleles (minor allele frequency [MAF] < 0.001) in genes intolerant of loss-of-function (LoF) variation and in genes whose messenger RNAs bind to fragile X mental retardation protein (FMRP). We further delineate the genetic architecture of schizophrenia by excluding a role for uncommon exomic variants (0.01 ≤ MAF ≥ 0.001) that confer a relatively large effect (odds ratio [OR] > 4). We also show risk alleles within this frequency range exist, but confer smaller effects and should be identified by larger studies.

The population genetics of human disease: The case of recessive, lethal mutations

Do the frequencies of disease mutations in human populations reflect a simple balance between mutation and purifying selection? What other factors shape the prevalence of disease mutations? To begin to answer these questions, we focused on one of the simplest cases: recessive mutations that alone cause lethal diseases or complete sterility. To this end, we generated a hand-curated set of 417 Mendelian mutations in 32 genes reported to cause a recessive, lethal Mendelian disease. We then considered analytic models of mutation-selection balance in infinite and finite populations of constant sizes and simulations of purifying selection in a more realistic demographic setting, and tested how well these models fit allele frequencies estimated from 33,370 individuals of European ancestry. In doing so, we distinguished between CpG transitions, which occur at a substantially elevated rate, and three other mutation types. Intriguingly, the observed frequency for CpG transitions is slightly higher than expectation but close, whereas the frequencies observed for the three other mutation types are an order of magnitude higher than expected, with a bigger deviation from expectation seen for less mutable types. This discrepancy is even larger when subtle fitness effects in heterozygotes or lethal compound heterozygotes are taken into account. In principle, higher than expected frequencies of disease mutations could be due to widespread errors in reporting causal variants, compensation by other mutations, or balancing selection. It is unclear why these factors would have a greater impact on disease mutations that occur at lower rates, however. We argue instead that the unexpectedly high frequency of disease mutations and the relationship to the mutation rate likely reflect an ascertainment bias: of all the mutations that cause recessive lethal diseases, those that by chance have reached higher frequencies are more likely to have been identified and thus to have been included in this study. Beyond the specific application, this study highlights the parameters likely to be important in shaping the frequencies of Mendelian disease alleles.

What determines the frequencies of disease mutations in human populations? To begin to answer this question, we focus on one of the simplest cases: mutations that cause completely recessive, lethal Mendelian diseases. We first review theory about what to expect from mutation and selection in a population of finite size and generate predictions based on simulations using a plausible demographic scenario of recent human evolution. For a highly mutable type of mutation, transitions at CpG sites, we find that the predictions are close to the observed frequencies of recessive lethal disease mutations. For less mutable types, however, predictions substantially under-estimate the observed frequency. We discuss possible explanations for the discrepancy and point to a complication that, to our knowledge, is not widely appreciated: that there exists ascertainment bias in disease mutation discovery. Specifically, we suggest that alleles that have been identified to date are likely the ones that by chance have reached higher frequencies and are thus more likely to have been mapped. More generally, our study highlights the factors that influence the frequencies of Mendelian disease alleles.

Identifying new antiepileptic drugs through genomics-based drug repurposing

Currently available antiepileptic drugs (AEDs) fail to control seizures in 30% of patients. Genomics-based drug repurposing (GBR) offers the potential of savings in the time and cost of developing new AEDs. In the current study, we used published data and software to identify the transcriptomic signature of chornic temporal lobe epilepsy and the drugs that reverse it. After filtering out compounds based on exclusion criteria, such as toxicity, 36 drugs were retained. 11 of the 36 drugs identified (>30%) have published evidence of the antiepileptic efficacy (for example, curcumin) or antiepileptogenic affect (for example, atorvastatin) in recognised rodent models or patients. By objectively annotating all ∼20,000 compounds in the LINCS database as either having published evidence of antiepileptic efficacy or lacking such evidence, we demonstrated that our set of repurposable drugs is ∼6-fold more enriched with drugs having published evidence of antiepileptic efficacy in animal models than expected by chance (P-value <0.006). Further, we showed that another of our GBR-identified drugs, the commonly-used well-tolerated antihyperglycemic sitagliptin, produces a dose-dependent reduction in seizures in a mouse model of pharmacoresistant epilepsy. In conclusion, GBR successfully identifies compounds with antiepileptic efficacy in animal models and, hence, it is an appealing methodology for the discovery of potential AEDs.

A meta-analysis of genome-wide association studies identifies 17 new Parkinson's disease risk loci

Common variant genome-wide association studies (GWASs) have, to date, identified >24 risk loci for Parkinson's disease (PD). To discover additional loci, we carried out a GWAS comparing 6,476 PD cases with 302,042 controls, followed by a meta-analysis with a recent study of over 13,000 PD cases and 95,000 controls at 9,830 overlapping variants. We then tested 35 loci (P < 1 × 10^-6) in a replication cohort of 5,851 cases and 5,866 controls. We identified 17 novel risk loci (P < 5 × 10^-8) in a joint analysis of 26,035 cases and 403,190 controls. We used a neurocentric strategy to assign candidate risk genes to the loci. We identified protein-altering or cis-expression quantitative trait locus (cis-eQTL) variants in linkage disequilibrium with the index variant in 29 of the 41 PD loci. These results indicate a key role for autophagy and lysosomal biology in PD risk, and suggest potential new drug targets for PD.

CAGI4 SickKids clinical genomes challenge: A pipeline for identifying pathogenic variants

Compared with earlier more restricted sequencing technologies, identification of rare disease variants using whole-genome sequence has the possibility of finding all causative variants, but issues of data quality and an overwhelming level of background variants complicate the analysis. The CAGI4 SickKids clinical genome challenge provided an opportunity to assess the landscape of variants found in a difficult set of 25 unsolved rare disease cases. To address the challenge, we developed a three-stage pipeline, first carefully analyzing data quality, then classifying high-quality gene-specific variants into seven categories, and finally examining each candidate variant for compatibility with the often complex phenotypes of these patients for final prioritization. Variants consistent with the phenotypes were found in 24 out of the 25 cases, and in a number of these, there are prioritized variants in multiple genes. Data quality analysis suggests that some of the selected variants are likely incorrect calls, complicating interpretation. The data providers followed up on three suggested variants with Sanger sequencing, and in one case, a prioritized variant was confirmed as likely causative by the referring physician, providing a diagnosis in a previously intractable case.

Combined immunodeficiency and atopy caused by a dominant negative mutation in caspase activation and recruitment domain family member 11 (CARD11)

BACKGROUND

Combined immunodeficiency (CID) is a T-cell defect frequently presenting with recurrent infections, as well as associated immune dysregulation manifesting as autoimmunity or allergic inflammation.

OBJECTIVE

We sought to identify the genetic aberration in 4 related patients with CID, early-onset asthma, eczema, and food allergies, as well as autoimmunity.

METHODS

We performed whole-exome sequencing, followed by Sanger confirmation, assessment of the genetic variant effect on cell signaling, and evaluation of the resultant immune function.

RESULTS

A heterozygous novel c.C88T 1-bp substitution resulting in amino acid change R30W in caspase activation and recruitment domain family member 11 (CARD11) was identified by using whole-exome sequencing and segregated perfectly to family members with severe atopy only but was not found in healthy subjects. We demonstrate that the R30W mutation results in loss of function while also exerting a dominant negative effect on wild-type CARD11. The CARD11 defect altered the classical nuclear factor κB pathway, resulting in poor in vitro T-cell responses to mitogens and antigens caused by reduced secretion of IFN-γ and IL-2.

CONCLUSION

Unlike patients with biallelic mutations in CARD11 causing severe CID, the R30W defect results in a less profound yet prominent susceptibility to infections, as well as multiorgan atopy and autoimmunity.

Early involvement of cellular stress and inflammatory signals in the pathogenesis of tubulointerstitial kidney disease due to UMOD mutations

Autosomal dominant tubulointerstitial kidney disease (ADTKD) is an inherited disorder that causes progressive kidney damage and renal failure. Mutations in the UMOD gene, encoding uromodulin, lead to ADTKD-UMOD related. Uromodulin is a GPI-anchored protein exclusively produced by epithelial cells of the thick ascending limb of Henle's loop. It is released in the tubular lumen after proteolytic cleavage and represents the most abundant protein in human urine in physiological condition. We previously generated and characterized a transgenic mouse model expressing mutant uromodulin (Tg ^UmodC147W) that recapitulates the main features of ATDKD-UMOD. While several studies clearly demonstrated that mutated uromodulin accumulates in endoplasmic reticulum, the mechanisms that lead to renal damage are not fully understood. In our work, we used kidney transcriptional profiling to identify early events of pathogenesis in the kidneys of Tg ^UmodC147W mice. Our results demonstrate up-regulation of inflammation and fibrosis and down-regulation of lipid metabolism in young Tg ^UmodC147W mice, before any functional or histological evidence of kidney damage. We also show that pro-inflammatory signals precede fibrosis onset and are already present in the first week after birth. Early induction of inflammation is likely relevant for ADTKD-UMOD pathogenesis and related pathways can be envisaged as possible novel targets for therapeutic intervention.

Exome sequencing revealed a splice site variant in the IQCE gene underlying post-axial polydactyly type A restricted to lower limb

Polydactyly is characterized by an extra supernumerary digit/toe with or without bony element. To date variants in four genes GLI3, ZNF141, MIPOL1 and PITX1 have been implicated in developing non-syndromic form of polydactyly. The present study involved characterization of large consanguineous family of Pakistani origin segregating post-axial polydactyly type A, restricted to lower limb, in autosomal recessive pattern. DNA of two affected members in the family was subjected to exome sequencing. Sanger sequencing was then followed to validate segregation of the variants in the family members. A homozygous splice acceptor site variant (c.395-1G>A) was identified in the IQCE gene, which completely co-segregated with post-axial polydactyly phenotype within the family. The homozygous variant was absent in different public variant databases, 7000 in-house exomes, 130 exomes from unrelated Pakistani individuals and 215 ethnically matched controls. Mini-gene splicing assay was used to test effect of the variant on function of the gene. The assay revealed loss of first nucleotide of exon 6, producing a -1 frameshift and a premature stop codon 22 bases downstream of the variant (p.Gly132Valfs*22). The study provided the first evidence of involvement of the IQCE gene in limbs development in humans.

Interrogating Patient-level Genomics and Mouse Phenomics towards Understanding Cytokines in Colorectal Cancer Metastasis

Background: Colorectal cancer is the second leading cancer-related death worldwide and a majority of patients die from metastasis. Chronic intestinal inflammation plays an important role in tumor progression of colorectal cancer. However, few study works on systematically predicting colorectal cancer metastasis using inflammatory cytokine genes. Results: We developed a supervised machine learning approach to predict colorectal cancer tumor progression using patient level genomic features. To better understand the role of cytokines, we integrated the metastatic-related genes from mouse phenotypic data. In addition, pathway analysis and network visualization were also applied to top significant genes ranked by feature weights of the final prediction model. The combined model of cytokines and mouse phenotypes achieved a predictive accuracy of 75.54%, higher than the model based on mouse phenotypes independently (70.42%, p-value<0.05). In additional, the combined model outperformed the model based on the existing metastatic-related epithelial-to-mesenchymal transition (EMT) genes (75.54% vs. 71.61%, p-value<0.05). We also observed that the most important cytokine gene features of the our model interact with the cancer driver genes and are highly associated with the colorectal cancer metastasis signaling pathway. Conclusion: We developed a combined model using both cytokine and mouse phenotype information to predict colorectal cancer metastasis. The results suggested that the inflammatory cytokines increase the power of predicting metastasis. We also systematically demonstrated the critical role of cytokines in progression of colorectal tumor.

Secreted Proteins Defy the Expression Level-Evolutionary Rate Anticorrelation

The rates of evolution of the proteins of any organism vary across orders of magnitude. A primary factor influencing rates of protein evolution is expression. A strong negative correlation between expression levels and evolutionary rates (the so-called E-R anticorrelation) has been observed in virtually all studied organisms. This effect is currently attributed to the abundance-dependent fitness costs of misfolding and unspecific protein-protein interactions, among other factors. Secreted proteins are folded in the endoplasmic reticulum, a compartment where chaperones, folding catalysts, and stringent quality control mechanisms promote their correct folding and may reduce the fitness costs of misfolding. In addition, confinement of secreted proteins to the extracellular space may reduce misinteractions and their deleterious effects. We hypothesize that each of these factors (the secretory pathway quality control and extracellular location) may reduce the strength of the E-R anticorrelation. Indeed, here we show that among human proteins that are secreted to the extracellular space, rates of evolution do not correlate with protein abundances. This trend is robust to controlling for several potentially confounding factors and is also observed when analyzing protein abundance data for 6 human tissues. In addition, analysis of mRNA abundance data for 32 human tissues shows that the E-R correlation is always less negative, and sometimes nonsignificant, in secreted proteins. Similar observations were made in Caenorhabditis elegans and in Escherichia coli, and to a lesser extent in Drosophila melanogaster, Saccharomyces cerevisiae and Arabidopsis thaliana. Our observations contribute to understand the causes of the E-R anticorrelation.

Epistatic Networks Jointly Influence Phenotypes Related to Metabolic Disease and Gene Expression in Diversity Outbred Mice

In this study, Tyler et al. analyzed the complex genetic architecture of metabolic disease-related traits using the Diversity Outbred mouse population

Genetic studies of multidimensional phenotypes can potentially link genetic variation, gene expression, and physiological data to create multi-scale models of complex traits. The challenge of reducing these data to specific hypotheses has become increasingly acute with the advent of genome-scale data resources. Multi-parent populations derived from model organisms provide a resource for developing methods to understand this complexity. In this study, we simultaneously modeled body composition, serum biomarkers, and liver transcript abundances from 474 Diversity Outbred mice. This population contained both sexes and two dietary cohorts. Transcript data were reduced to functional gene modules with weighted gene coexpression network analysis (WGCNA), which were used as summary phenotypes representing enriched biological processes. These module phenotypes were jointly analyzed with body composition and serum biomarkers in a combined analysis of pleiotropy and epistasis (CAPE), which inferred networks of epistatic interactions between quantitative trait loci that affect one or more traits. This network frequently mapped interactions between alleles of different ancestries, providing evidence of both genetic synergy and redundancy between haplotypes. Furthermore, a number of loci interacted with sex and diet to yield sex-specific genetic effects and alleles that potentially protect individuals from the effects of a high-fat diet. Although the epistatic interactions explained small amounts of trait variance, the combination of directional interactions, allelic specificity, and high genomic resolution provided context to generate hypotheses for the roles of specific genes in complex traits. Our approach moves beyond the cataloging of single loci to infer genetic networks that map genetic etiology by simultaneously modeling all phenotypes.

Neurodevelopmental protein Musashi-1 interacts with the Zika genome and promotes viral replication

A recent outbreak of Zika virus in Brazil has led to a simultaneous increase in reports of neonatal microcephaly. Zika targets cerebral neural precursors, a cell population essential for cortical development, but the cause of this neurotropism remains obscure. Here we report that the neural RNA-binding protein Musashi-1 (MSI1) interacts with the Zika genome and enables viral replication. Zika infection disrupts the binding of MSI1 to its endogenous targets, thereby deregulating expression of factors implicated in neural stem cell function. We further show that MSI1 is highly expressed in neural progenitors of the human embryonic brain and is mutated in individuals with autosomal recessive primary microcephaly. Selective MSI1 expression in neural precursors could therefore explain the exceptional vulnerability of these cells to Zika infection.

Mouse Genome Informatics (MGI) Resource: Genetic, Genomic, and Biological Knowledgebase for the Laboratory Mouse

The Mouse Genome Informatics (MGI) Resource supports basic, translational, and computational research by providing high-quality, integrated data on the genetics, genomics, and biology of the laboratory mouse. MGI serves a strategic role for the scientific community in facilitating biomedical, experimental, and computational studies investigating the genetics and processes of diseases and enabling the development and testing of new disease models and therapeutic interventions. This review describes the nexus of the body of growing genetic and biological data and the advances in computer technology in the late 1980s, including the World Wide Web, that together launched the beginnings of MGI. MGI develops and maintains a gold-standard resource that reflects the current state of knowledge, provides semantic and contextual data integration that fosters hypothesis testing, continually develops new and improved tools for searching and analysis, and partners with the scientific community to assure research data needs are met. Here we describe one slice of MGI relating to the development of community-wide large-scale mutagenesis and phenotyping projects and introduce ways to access and use these MGI data. References and links to additional MGI aspects are provided.

Classification and analysis of a large collection of in vivo bioassay descriptions

Testing potential drug treatments in animal disease models is a decisive step of all preclinical drug discovery programs. Yet, despite the importance of such experiments for translational medicine, there have been relatively few efforts to comprehensively and consistently analyze the data produced by in vivo bioassays. This is partly due to their complexity and lack of accepted reporting standards-publicly available animal screening data are only accessible in unstructured free-text format, which hinders computational analysis. In this study, we use text mining to extract information from the descriptions of over 100,000 drug screening-related assays in rats and mice. We retrieve our dataset from ChEMBL-an open-source literature-based database focused on preclinical drug discovery. We show that in vivo assay descriptions can be effectively mined for relevant information, including experimental factors that might influence the outcome and reproducibility of animal research: genetic strains, experimental treatments, and phenotypic readouts used in the experiments. We further systematize extracted information using unsupervised language model (Word2Vec), which learns semantic similarities between terms and phrases, allowing identification of related animal models and classification of entire assay descriptions. In addition, we show that random forest models trained on features generated by Word2Vec can predict the class of drugs tested in different in vivo assays with high accuracy. Finally, we combine information mined from text with curated annotations stored in ChEMBL to investigate the patterns of usage of different animal models across a range of experiments, drug classes, and disease areas.

Mouse Genome-Wide Association Study of Preclinical Group II Pulmonary Hypertension Identifies Epidermal Growth Factor Receptor

Pulmonary hypertension (PH) is associated with features of obesity and metabolic syndrome that translate to the induction of PH by chronic high-fat diet (HFD) in some inbred mouse strains. We conducted a genome-wide association study (GWAS) to identify candidate genes associated with susceptibility to HFD-induced PH. Mice from 36 inbred and wild-derived strains were fed with regular diet or HFD for 20 weeks beginning at 6-12 weeks of age, after which right ventricular (RV) and left ventricular (LV) end-systolic pressure (ESP) and maximum pressure (MaxP) were measured by cardiac catheterization. We tested for association of RV MaxP and RV ESP and identified genomic regions enriched with nominal associations to both of these phenotypes. We excluded genomic regions if they were also associated with LV MaxP, LV ESP, or body weight. Genes within significant regions were scored based on the shortest-path betweenness centrality, a measure of network connectivity, of their human orthologs in a gene interaction network of human PH-related genes. WSB/EiJ, NON/ShiLtJ, and AKR/J mice had the largest increases in RV MaxP after high-fat feeding. Network-based scoring of GWAS candidates identified epidermal growth factor receptor (Egfr) as having the highest shortest-path betweenness centrality of GWAS candidates. Expression studies of lung homogenate showed that EGFR expression is increased in the AKR/J strain, which developed a significant increase in RV MaxP after high-fat feeding as compared with C57BL/6J, which did not. Our combined GWAS and network-based approach adds evidence for a role for Egfr in murine PH.

A Comprehensive Mouse Transcriptomic BodyMap across 17 Tissues by RNA-seq

The mouse has been widely used as a model organism for studying human diseases and for evaluating drug safety and efficacy. Many diseases and drug effects exhibit tissue specificity that may be reflected by tissue-specific gene-expression profiles. Here we construct a comprehensive mouse transcriptomic BodyMap across 17 tissues of six-weeks old C57BL/6JJcl mice using RNA-seq. We find different expression patterns between protein-coding and non-coding genes. Liver expressed the least complex transcriptomes, that is, the smallest number of genes detected in liver across all 17 tissues, whereas testis and ovary harbor more complex transcriptomes than other tissues. We report a comprehensive list of tissue-specific genes across 17 tissues, along with a list of 4,781 housekeeping genes in mouse. In addition, we propose a list of 27 consistently and highly expressed genes that can be used as reference controls in expression-profiling analysis. Our study provides a unique resource of mouse gene-expression profiles, which is helpful for further biomedical research.

Sequence analysis of chromosome 1 revealed different selection patterns between Chinese wild mice and laboratory strains

Both natural and artificial selection play a critical role in animals' adaptation to the environment. Detection of the signature of selection in genomic regions can provide insights for understanding the function of specific phenotypes. It is generally assumed that laboratory mice may experience intense artificial selection while wild mice more natural selection. However, the differences of selection signature in the mouse genome and underlying genes between wild and laboratory mice remain unclear. In this study, we used two mouse populations: chromosome 1 (Chr 1) substitution lines (C1SLs) derived from Chinese wild mice and mouse genome project (MGP) sequenced inbred strains and two selection detection statistics: Fst and Tajima's D to identify the signature of selection footprint on Chr 1. For the differentiation between the C1SLs and MGP, 110 candidate selection regions containing 47 protein coding genes were detected. A total of 149 selection regions which encompass 7.215 Mb were identified in the C1SLs by Tajima's D approach. While for the MGP, we identified nearly twice selection regions (243) compared with the C1SLs which accounted for 13.27 Mb Chr 1 sequence. Through functional annotation, we identified several biological processes with significant enrichment including seven genes in the olfactory transduction pathway. In addition, we searched the phenotypes associated with the 47 candidate selection genes identified by Fst. These genes were involved in behavior, growth or body weight, mortality or aging, and immune systems which align well with the phenotypic differences between wild and laboratory mice. Therefore, the findings would be helpful for our understanding of the phenotypic differences between wild and laboratory mice and applications for using this new mouse resource (C1SLs) for further genetics studies.

Association of VLDLR haplotypes with abdominal fat trait in ducks

Abstract. This study aimed to determine the correlation among VLDLR (very low-density lipoprotein receptor) gene polymorphisms, body weight and abdominal fat deposition of Gaoyou ducks. A total of 267 Gaoyou ducks from one pure line was employed for testing. The polymorphisms of the VLDLR gene were screened by polymerase chain reaction and DNA sequencing. Four novel single nucleotide polymorphisms (SNPs) (g.151G  >  A, g.170C  >  T, g.206A  >  G and g.278–295del) were identified in the 5'-UTR and signal peptide region. Furthermore, eight haplotypes were identified based on the four SNPs. The H8 was the most common haplotype with a frequency of more than 31 %. The four SNPs and their haplotype combinations were shown to be significantly associated with body weight at 6–10 weeks of age (P < 0. 05 or P < 0. 01) and abdominal fat percentage (AFP) (P < 0. 05 or P < 0. 01). Remarkably, the H1H1 diplotype had an effect on increasing body weight and decreasing AFP from the 6th to the 10th weeks of age. However, increasing positive effects of the H5H8 diplotype were observed for both body weight and AFP. This study suggests that the VLDLR gene plays an important role in the regulation of body weight and fat-related traits and may serve as a potential marker for the marker-assisted selection program during duck breeding.

The nematode homologue of Mediator complex subunit 28, F28F8.5, is a critical regulator of C. elegans development

The evolutionarily conserved Mediator complex is a critical player in regulating transcription. Comprised of approximately two dozen proteins, the Mediator integrates diverse regulatory signals through direct protein-protein interactions that, in turn, modulate the influence of Mediator on RNA Polymerase II activity. One Mediator subunit, MED28, is known to interact with cytoplasmic structural proteins, providing a potential direct link between cytoplasmic dynamics and the control of gene transcription. Although identified in many animals and plants, MED28 is not present in yeast; no bona fide MED28 has been described previously in Caenorhabditis elegans. Here, we identify bioinformatically F28F8.5, an uncharacterized predicted protein, as the nematode homologue of MED28. As in other Metazoa, F28F8.5 has dual nuclear and cytoplasmic localization and plays critical roles in the regulation of development. F28F8.5 is a vital gene and its null mutants have severely malformed gonads and do not reproduce. F28F8.5 interacts on the protein level with the Mediator subunits MDT-6 and MDT-30. Our results indicate that F28F8.5 is an orthologue of MED28 and suggest that the potential to link cytoplasmic and nuclear events is conserved between MED28 vertebrate and nematode orthologues.

Remodeling adipose tissue through in silico modulation of fat storage for the prevention of type 2 diabetes

BACKGROUND

Type 2 diabetes is one of the leading non-infectious diseases worldwide and closely relates to excess adipose tissue accumulation as seen in obesity. Specifically, hypertrophic expansion of adipose tissues is related to increased cardiometabolic risk leading to type 2 diabetes. Studying mechanisms underlying adipocyte hypertrophy could lead to the identification of potential targets for the treatment of these conditions.

RESULTS

We present iTC1390adip, a highly curated metabolic network of the human adipocyte presenting various improvements over the previously published iAdipocytes1809. iTC1390adip contains 1390 genes, 4519 reactions and 3664 metabolites. We validated the network obtaining 92.6% accuracy by comparing experimental gene essentiality in various cell lines to our predictions of biomass production. Using flux balance analysis under various test conditions, we predict the effect of gene deletion on both lipid droplet and biomass production, resulting in the identification of 27 genes that could reduce adipocyte hypertrophy. We also used expression data from visceral and subcutaneous adipose tissues to compare the effect of single gene deletions between adipocytes from each compartment.

CONCLUSIONS

We generated a highly curated metabolic network of the human adipose tissue and used it to identify potential targets for adipose tissue metabolic dysfunction leading to the development of type 2 diabetes.

Naming CRISPR alleles: endonuclease-mediated mutation nomenclature across species

The widespread use of CRISPR/Cas and other targeted endonuclease technologies in many species has led to an explosion in the generation of new mutations and alleles. The ability to generate many different mutations from the same target sequence either by homology-directed repair with a donor sequence or non-homologous end joining-induced insertions and deletions necessitates a means for representing these mutations in literature and databases. Standardized nomenclature can be used to generate unambiguous, concise, and specific symbols to represent mutations and alleles. The research communities of a variety of species using CRISPR/Cas and other endonuclease-mediated mutation technologies have developed different approaches to naming and identifying such alleles and mutations. While some organism-specific research communities have developed allele nomenclature that incorporates the method of generation within the official allele or mutant symbol, others use metadata tags that include method of generation or mutagen. Organism-specific research community databases together with organism-specific nomenclature committees are leading the way in providing standardized nomenclature and metadata to facilitate the integration of data from alleles and mutations generated using CRISPR/Cas and other targeted endonucleases.

Lung epithelial cells have virus-specific and shared gene expression responses to infection by diverse respiratory viruses

The severity of respiratory viral infections is partially determined by the cellular response mounted by infected lung epithelial cells. Disease prevention and treatment is dependent on our understanding of the shared and unique responses elicited by diverse viruses, yet few studies compare host responses to viruses from different families while controlling other experimental parameters. Murine models are commonly used to study the pathogenesis of respiratory viral infections, and in vitro studies using murine cells provide mechanistic insight into the pathogenesis observed in vivo. We used microarray analysis to compare changes in gene expression of murine lung epithelial cells infected individually by three respiratory viruses causing mild (rhinovirus, RV1B), moderate (coronavirus, MHV-1), and severe (influenza A virus, PR8) disease in mice. RV1B infection caused numerous gene expression changes, but the differential effect peaked at 12 hours post-infection. PR8 altered an intermediate number of genes whose expression continued to change through 24 hours. MHV-1 had comparatively few effects on host gene expression. The viruses elicited highly overlapping responses in antiviral genes, though MHV-1 induced a lower type I interferon response than the other two viruses. Signature genes were identified for each virus and included host defense genes for PR8, tissue remodeling genes for RV1B, and transcription factors for MHV-1. Our comparative approach identified universal and specific transcriptional signatures of virus infection that can be used to distinguish shared and virus-specific mechanisms of pathogenesis in the respiratory tract.

Considerations for Experimental Animal Models of Concussion, Traumatic Brain Injury, and Chronic Traumatic Encephalopathy-These Matters Matter

Animal models of concussion, traumatic brain injury (TBI), and chronic traumatic encephalopathy (CTE) are widely available and routinely deployed in laboratories around the world. Effective animal modeling requires careful consideration of four basic principles. First, animal model use must be guided by clarity of definitions regarding the human disease or condition being modeled. Concussion, TBI, and CTE represent distinct clinical entities that require clear differentiation: concussion is a neurological syndrome, TBI is a neurological event, and CTE is a neurological disease. While these conditions are all associated with head injury, the pathophysiology, clinical course, and medical management of each are distinct. Investigators who use animal models of these conditions must take into account these clinical distinctions to avoid misinterpretation of results and category mistakes. Second, model selection must be grounded by clarity of purpose with respect to experimental questions and frame of reference of the investigation. Distinguishing injury context ("inputs") from injury consequences ("outputs") may be helpful during animal model selection, experimental design and execution, and interpretation of results. Vigilance is required to rout out, or rigorously control for, model artifacts with potential to interfere with primary endpoints. The widespread use of anesthetics in many animal models illustrates the many ways that model artifacts can confound preclinical results. Third, concordance between key features of the animal model and the human disease or condition being modeled is required to confirm model biofidelity. Fourth, experimental results observed in animals must be confirmed in human subjects for model validation. Adherence to these principles serves as a bulwark against flawed interpretation of results, study replication failure, and confusion in the field. Implementing these principles will advance basic science discovery and accelerate clinical translation to benefit people affected by concussion, TBI, and CTE.

ALOX5 exhibits anti-tumor and drug-sensitizing effects in MLL-rearranged leukemia

MLL-rearranged acute myeloid leukemia (AML) remains a fatal disease with a high rate of relapse and therapeutic failure due to chemotherapy resistance. In analysis of our Affymetrix microarray profiling and chromatin immunoprecipitation (ChIP) assays, we found that ALOX5 is especially down-regulated in MLL-rearranged AML, via transcription repression mediated by Polycomb repressive complex 2 (PRC2). Colony forming/replating and bone marrow transplantation (BMT) assays showed that Alox5 exhibited a moderate anti-tumor effect both in vitro and in vivo. Strikingly, leukemic cells with Alox5 overexpression showed a significantly higher sensitivity to the standard chemotherapeutic agents, i.e., doxorubicin (DOX) and cytarabine (Ara-C). The drug-sensitizing role of Alox5 was further confirmed in human and murine MLL-rearranged AML cell models in vitro, as well as in the in vivo MLL-rearranged AML BMT model coupled with treatment of “5 + 3” (i.e. DOX plus Ara-C) regimen. Stat and K-Ras signaling pathways were negatively correlated with Alox5 overexpression in MLL-AF9-leukemic blast cells; inhibition of the above signaling pathways mimicked the drug-sensitizing effect of ALOX5 in AML cells. Collectively, our work shows that ALOX5 plays a moderate anti-tumor role and functions as a drug sensitizer, with a therapeutic potential, in MLL-rearranged AML.

Non invasive imaging assessment of the biodistribution of GSK2849330, an ADCC and CDC optimized anti HER3 mAb, and its role in tumor macrophage recruitment in human tumor-bearing mice

The purpose of this work was to use various molecular imaging techniques to non-invasively assess GSK2849330 (anti HER3 ADCC and CDC enhanced ‘AccretaMab’ monoclonal antibody) pharmacokinetics and pharmacodynamics in human xenograft tumor-bearing mice. Immuno-PET biodistribution imaging of radiolabeled ⁸⁹Zr-GSK2849330 was assessed in mice with HER3 negative (MIA-PaCa-2) and positive (CHL-1) human xenograft tumors. Dose dependency of GSK2849330 disposition was assessed using varying doses of unlabeled GSK2849330 co-injected with ⁸⁹Zr-GSK2849330. In-vivo NIRF optical imaging and ex-vivo confocal microscopy were used to assess the biodistribution of GSK2849330 and the HER3 receptor occupancy in HER3 positive xenograft tumors (BxPC3, and CHL-1). Ferumoxytol (USPIO) contrast-enhanced MRI was used to investigate the effects of GSK2849330 on tumor macrophage content in CHL-1 xenograft bearing mice. Immuno-PET imaging was used to monitor the whole body drug biodistribution and CHL-1 xenograft tumor uptake up to 144 hours post injection of ⁸⁹Zr-GSK2849330. Both hepatic and tumor uptake were dose dependent and saturable. The optical imaging data in the BxPC3 xenograft tumor confirmed the tumor dose response finding in the Immuno-PET study. Confocal microscopy showed a distinguished cytoplasmic punctate staining pattern within individual CHL-1 cells. GSK2849330 inhibited tumor growth and this was associated with a significant decrease in MRI signal to noise ratio after USPIO injection and with a significant increase in tumor macrophages as confirmed by a quantitative immunohistochemistry analysis. By providing both dose response and time course data from both ⁸⁹Zr and fluorescently labeled GSK2849330, complementary imaging studies were used to characterize GSK2849330 biodistribution and tumor uptake in vivo. Ferumoxytol-enhanced MRI was used to monitor aspects of the immune system response to GSK2849330. Together these approaches potentially provide clinically translatable, non-invasive techniques to support dose optimization, and assess immune activation and anti-tumor responses.

Detection of QTL for traits related to adaptation to sub-optimal climatic conditions in chickens

BACKGROUND

Growth traits can be used as indicators of adaptation to sub-optimal conditions. The current study aimed at identifying quantitative trait loci (QTL) that control performance under variable temperature conditions in chickens.

METHODS

An F2 population was produced by crossing the Taiwan Country chicken L2 line (selected for body weight, comb area, and egg production) with an experimental line of Rhode Island Red layer R- (selected for low residual feed consumption). A total of 844 animals were genotyped with the 60 K Illumina single nucleotide polymorphism (SNP) chip. Whole-genome interval linkage mapping and a genome-wide association study (GWAS) were performed for body weight at 0, 4, 8, 12, and 16 weeks of age, shank length at 8 weeks of age, size of comb area at 16 weeks of age, and antibody response to sheep red blood cells at 11 weeks of age (7 and 14 days after primary immunization). Relevant genes were identified based on functional annotation of candidate genes and potentially relevant SNPs were detected by comparing whole-genome sequences of several birds between the parental lines.

RESULTS

Whole-genome QTL analysis revealed 47 QTL and 714 effects associated with 178 SNPs were identified by GWAS with 5% Bonferroni genome-wide significance. Little overlap was observed between the QTL and GWAS results, with only two chromosomal regions detected by both approaches, i.e. one on GGA24 (GGA for Gallus gallus chromosome) for BW04 and one on GGAZ for six growth-related traits. Based on whole-genome sequence, differences between the parental lines based on several birds were screened in the genome-wide QTL regions and in a region detected by both methods, resulting in the identification of 106 putative candidate genes with a total of 15,443 SNPs, of which 41 were missense and 1698 were not described in the dbSNP archive.

CONCLUSIONS

The QTL detected in this study for growth and morphological traits likely influence adaptation of chickens to sub-tropical climate. Using whole-genome sequence data, we identified candidate SNPs for further confirmation of QTL in the F2 design. A strong QTL effect found on GGAZ underlines the importance of sex-linked inheritance for growth traits in chickens.

TUFT1, a novel candidate gene for metatarsophalangeal osteoarthritis, plays a role in chondrogenesis on a calcium-related pathway

Osteoarthritis (OA) is the most common degenerative joint disorder and genetic factors have been shown to have a significant role in its etiology. The first metatarsophalangeal joint (MTP I) is highly susceptible to development of OA due to repetitive mechanical stress during walking. We used whole exome sequencing to study genetic defect(s) predisposing to familial early-onset bilateral MTP I OA inherited in an autosomal dominant manner. A nonsynonymous single nucleotide variant rs41310883 (c.524C>T, p.Thr175Met) in TUFT1 gene was found to co-segregate perfectly with MTP I OA. The role of TUFT1 and the relevance of the identified variant in pathogenesis of MTP I OA were further assessed using functional in vitro analyses. The variant reduced TUFT1 mRNA and tuftelin protein expression in HEK293 cells. ATDC5 cells overexpressing wild type (wt) or mutant TUFT1 were cultured in calcifying conditions and chondrogenic differentiation was found to be inhibited in both cell populations, as indicated by decreased marker gene expression when compared with the empty vector control cells. Also, the formation of cartilage nodules was diminished in both TUFT1 overexpressing ATDC5 cell populations. At the end of the culturing period the calcium content of the extracellular matrix was significantly increased in cells overexpressing mutant TUFT1 compared to cells overexpressing wt TUFT1 and control cells, while the proteoglycan content was reduced. These data imply that overexpression of TUFT1 in ATDC5 inhibits chondrogenic differentiation, and the identified variant may contribute to the pathogenesis of OA by increasing calcification and reducing amount of proteoglycans in the articular cartilage extracellular matrix thus making cartilage susceptible for degeneration and osteophyte formation.

Defining the transcriptomic landscape of the developing enteric nervous system and its cellular environment

BACKGROUND

Motility and the coordination of moving food through the gastrointestinal tract rely on a complex network of neurons known as the enteric nervous system (ENS). Despite its critical function, many of the molecular mechanisms that direct the development of the ENS and the elaboration of neural network connections remain unknown. The goal of this study was to transcriptionally identify molecular pathways and candidate genes that drive specification, differentiation and the neural circuitry of specific neural progenitors, the phox2b expressing ENS cell lineage, during normal enteric nervous system development. Because ENS development is tightly linked to its environment, the transcriptional landscape of the cellular environment of the intestine was also analyzed.

RESULTS

Thousands of zebrafish intestines were manually dissected from a transgenic line expressing green fluorescent protein under the phox2b regulatory elements [Tg(phox2b:EGFP) ^w37 ]. Fluorescence-activated cell sorting was used to separate GFP-positive phox2b expressing ENS progenitor and derivatives from GFP-negative intestinal cells. RNA-seq was performed to obtain accurate, reproducible transcriptional profiles and the unbiased detection of low level transcripts. Analysis revealed genes and pathways that may function in ENS cell determination, genes that may be identifiers of different ENS subtypes, and genes that define the non-neural cellular microenvironment of the ENS. Differential expression analysis between the two cell populations revealed the expected neuronal nature of the phox2b expressing lineage including the enrichment for genes required for neurogenesis and synaptogenesis, and identified many novel genes not previously associated with ENS development. Pathway analysis pointed to a high level of G-protein coupled pathway activation, and identified novel roles for candidate pathways such as the Nogo/Reticulon axon guidance pathway in ENS development.

CONCLUSION

We report the comprehensive gene expression profiles of a lineage-specific population of enteric progenitors, their derivatives, and their microenvironment during normal enteric nervous system development. Our results confirm previously implicated genes and pathways required for ENS development, and also identify scores of novel candidate genes and pathways. Thus, our dataset suggests various potential mechanisms that drive ENS development facilitating characterization and discovery of novel therapeutic strategies to improve gastrointestinal disorders.

Human knockouts and phenotypic analysis in a cohort with a high rate of consanguinity

A major goal of biomedicine is to understand the function of every gene in the human genome. Loss-of-function mutations can disrupt both copies of a given gene in humans and phenotypic analysis of such 'human knockouts' can provide insight into gene function. Consanguineous unions are more likely to result in offspring carrying homozygous loss-of-function mutations. In Pakistan, consanguinity rates are notably high. Here we sequence the protein-coding regions of 10,503 adult participants in the Pakistan Risk of Myocardial Infarction Study (PROMIS), designed to understand the determinants of cardiometabolic diseases in individuals from South Asia. We identified individuals carrying homozygous predicted loss-of-function (pLoF) mutations, and performed phenotypic analysis involving more than 200 biochemical and disease traits. We enumerated 49,138 rare (<1% minor allele frequency) pLoF mutations. These pLoF mutations are estimated to knock out 1,317 genes, each in at least one participant. Homozygosity for pLoF mutations at PLA2G7 was associated with absent enzymatic activity of soluble lipoprotein-associated phospholipase A2; at CYP2F1, with higher plasma interleukin-8 concentrations; at TREH, with lower concentrations of apoB-containing lipoprotein subfractions; at either A3GALT2 or NRG4, with markedly reduced plasma insulin C-peptide concentrations; and at SLC9A3R1, with mediators of calcium and phosphate signalling. Heterozygous deficiency of APOC3 has been shown to protect against coronary heart disease; we identified APOC3 homozygous pLoF carriers in our cohort. We recruited these human knockouts and challenged them with an oral fat load. Compared with family members lacking the mutation, individuals with APOC3 knocked out displayed marked blunting of the usual post-prandial rise in plasma triglycerides. Overall, these observations provide a roadmap for a 'human knockout project', a systematic effort to understand the phenotypic consequences of complete disruption of genes in humans.

Inclusion of biological knowledge in a Bayesian shrinkage model for joint estimation of SNP effects

With the aim of improving detection of novel single-nucleotide polymorphisms (SNPs) in genetic association studies, we propose a method of including prior biological information in a Bayesian shrinkage model that jointly estimates SNP effects. We assume that the SNP effects follow a normal distribution centered at zero with variance controlled by a shrinkage hyperparameter. We use biological information to define the amount of shrinkage applied on the SNP effects distribution, so that the effects of SNPs with more biological support are less shrunk toward zero, thus being more likely detected. The performance of the method was tested in a simulation study (1,000 datasets, 500 subjects with ∼200 SNPs in 10 linkage disequilibrium (LD) blocks) using a continuous and a binary outcome. It was further tested in an empirical example on body mass index (continuous) and overweight (binary) in a dataset of 1,829 subjects and 2,614 SNPs from 30 blocks. Biological knowledge was retrieved using the bioinformatics tool Dintor, which queried various databases. The joint Bayesian model with inclusion of prior information outperformed the standard analysis: in the simulation study, the mean ranking of the true LD block was 2.8 for the Bayesian model versus 3.6 for the standard analysis of individual SNPs; in the empirical example, the mean ranking of the six true blocks was 8.5 versus 9.3 in the standard analysis. These results suggest that our method is more powerful than the standard analysis. We expect its performance to improve further as more biological information about SNPs becomes available.

AHCODA-DB: a data repository with web-based mining tools for the analysis of automated high-content mouse phenomics data

BACKGROUND

Systematic, standardized and in-depth phenotyping and data analyses of rodent behaviour empowers gene-function studies, drug testing and therapy design. However, no data repositories are currently available for standardized quality control, data analysis and mining at the resolution of individual mice.

DESCRIPTION

Here, we present AHCODA-DB, a public data repository with standardized quality control and exclusion criteria aimed to enhance robustness of data, enabled with web-based mining tools for the analysis of individually and group-wise collected mouse phenotypic data. AHCODA-DB allows monitoring in vivo effects of compounds collected from conventional behavioural tests and from automated home-cage experiments assessing spontaneous behaviour, anxiety and cognition without human interference. AHCODA-DB includes such data from mutant mice (transgenics, knock-out, knock-in), (recombinant) inbred strains, and compound effects in wildtype mice and disease models. AHCODA-DB provides real time statistical analyses with single mouse resolution and versatile suite of data presentation tools. On March 9th, 2017 AHCODA-DB contained 650 k data points on 2419 parameters from 1563 mice.

CONCLUSION

AHCODA-DB provides users with tools to systematically explore mouse behavioural data, both with positive and negative outcome, published and unpublished, across time and experiments with single mouse resolution. The standardized (automated) experimental settings and the large current dataset (1563 mice) in AHCODA-DB provide a unique framework for the interpretation of behavioural data and drug effects. The use of common ontologies allows data export to other databases such as the Mouse Phenome Database. Unbiased presentation of positive and negative data obtained under the highly standardized screening conditions increase cost efficiency of publicly funded mouse screening projects and help to reach consensus conclusions on drug responses and mouse behavioural phenotypes. The website is publicly accessible through https://public.sylics.com and can be viewed in every recent version of all commonly used browsers.

A murine preclinical syngeneic transplantation model for breast cancer precision medicine

We previously demonstrated that altered activity of lysophosphatidic acid in murine mammary glands promotes tumorigenesis. We have now established and characterized a heterogeneous collection of mouse-derived syngeneic transplants (MDSTs) as preclinical platforms for the assessment of personalized pharmacological therapies. Detailed molecular and phenotypic analyses revealed that MDSTs are the most heterogeneous group of genetically engineered mouse models (GEMMs) of breast cancer yet observed. Response of MDSTs to trametinib, a mitogen-activated protein kinase (MAPK) kinase inhibitor, correlated with RAS/MAPK signaling activity, as expected from studies in xenografts and clinical trials providing validation of the utility of the model. Sensitivity of MDSTs to talazoparib, a poly(adenosine 5'-diphosphate-ribose) polymerase (PARP) inhibitor, was predicted by PARP1 protein levels and by a new PARP sensitivity predictor (PSP) score developed from integrated analysis of drug sensitivity data of human cell lines. PSP score-based classification of The Cancer Genome Atlas breast cancer suggested that a subset of patients with limited therapeutic options would be expected to benefit from PARP-targeted drugs. These results indicate that MDSTs are useful models for studies of targeted therapies, and propose novel potential biomarkers for identification of breast cancer patients likely to benefit from personalized pharmacological treatments.

A de novo nonsense mutation in ZBTB18 plus a de novo 15q13.3 microdeletion in a 6-year-old female

ZBTB18 has been proposed as candidate gene for microcephaly and abnormalities of the corpus callosum based on overlapping microdeletions of 1q43q44. More recently, de novo mutations of ZBTB18 have been identified in patients with syndromic and non-syndromic intellectual disability. Heterozygous microdeletions of 15q13.3 encompassing the candidate gene CHRNA7 are associated with developmental delay or intellectual disability with speech problems, hypotonia, and seizures. They are characterized by significant variability and reduced penetrance. We report on a patient with a de novo ZBTB18 nonsense mutation and a de novo 15q13.3 microdeletion, both in a heterozygous state, identified by next generation sequencing and array-CGH. The 6-year-old girl showed global developmental delay, absent speech, therapy-refractory seizures, ataxia, muscular hypotonia, and discrete facial dysmorphisms. Almost all of these features have been reported for both genetic aberrations, but the severity could hardly been explained by the microdeletion 15q13.3 alone. We assume an additive effect of haploinsufficiency of ZBTB18 and CHRNA7 in our patient. Assembling the features of our patient and the published patients, we noted that only one of them showed mild anomalies of the corpus callosum. Moreover, we hypothesize that nonsense mutations of ZBTB18 are associated with a more severe phenotype than missense mutations. This report indicates that haploinsufficiency of additional genes beside ZBTB18 causes the high frequency of corpus callosum anomalies in patients with microdeletions of 1q43q44 and underlines the importance of an NGS-based molecular diagnostic in complex phenotypes.

Identification of common non-coding variants at 1p22 that are functional for non-syndromic orofacial clefting

Genome-wide association studies (GWAS) do not distinguish between single nucleotide polymorphisms (SNPs) that are causal and those that are merely in linkage-disequilibrium with causal mutations. Here we describe a versatile, functional pipeline and apply it to SNPs at 1p22, a locus identified in several GWAS for non-syndromic cleft lip with or without cleft palate (NS CL/P). First we amplified DNA elements containing the ten most-highly risk-associated SNPs and tested their enhancer activity in vitro, identifying three SNPs with allele-dependent effects on such activity. We then used in vivo reporter assays to test the tissue-specificity of these enhancers, chromatin configuration capture to test enhancer-promoter interactions, and genome editing in vitro to show allele-specific effects on ARHGAP29 expression and cell migration. Our results further indicate that two SNPs affect binding of CL/P-associated transcription factors, and one affects chromatin configuration. These results translate risk into potential mechanisms of pathogenesis.

Effects of different kinds of essentiality on sequence evolution of human testis proteins

We asked if essentiality for either fertility or viability differentially affects sequence evolution of human testis proteins. Based on murine knockout data, we classified a set of 965 proteins expressed in human seminiferous tubules into three categories: proteins essential for prepubertal survival (“lethality proteins”), associated with male sub- or infertility (“male sub-/infertility proteins”), and nonessential proteins. In our testis protein dataset, lethality genes evolved significantly slower than nonessential and male sub-/infertility genes, which is in line with other authors’ findings. Using tissue specificity, connectivity in the protein-protein interaction (PPI) network, and multifunctionality as proxies for evolutionary constraints, we found that of the three categories, proteins linked to male sub- or infertility are least constrained. Lethality proteins, on the other hand, are characterized by broad expression, many PPI partners, and high multifunctionality, all of which points to strong evolutionary constraints. We conclude that compared with lethality proteins, those linked to male sub- or infertility are nonetheless indispensable, but evolve under more relaxed constraints. Finally, adaptive evolution in response to postmating sexual selection could further accelerate evolutionary rates of male sub- or infertility proteins expressed in human testis. These findings may become useful for in silico detection of human sub-/infertility genes.

ExCAPE-DB: an integrated large scale dataset facilitating Big Data analysis in chemogenomics

Chemogenomics data generally refers to the activity data of chemical compounds on an array of protein targets and represents an important source of information for building in silico target prediction models. The increasing volume of chemogenomics data offers exciting opportunities to build models based on Big Data. Preparing a high quality data set is a vital step in realizing this goal and this work aims to compile such a comprehensive chemogenomics dataset. This dataset comprises over 70 million SAR data points from publicly available databases (PubChem and ChEMBL) including structure, target information and activity annotations. Our aspiration is to create a useful chemogenomics resource reflecting industry-scale data not only for building predictive models of in silico polypharmacology and off-target effects but also for the validation of cheminformatics approaches in general.

Rare copy number variants in patients with congenital conotruncal heart defects

BACKGROUND

Previous studies using different cardiac phenotypes, technologies and designs suggest a burden of large, rare or de novo copy number variants (CNVs) in subjects with congenital heart defects. We sought to identify disease-related CNVs, candidate genes, and functional pathways in a large number of cases with conotruncal and related defects that carried no known genetic syndrome.

METHODS

Cases and control samples were divided into two cohorts and genotyped to assess each subject's CNV content. Analyses were performed to ascertain differences in overall CNV prevalence and to identify enrichment of specific genes and functional pathways in conotruncal cases relative to healthy controls.

RESULTS

Only findings present in both cohorts are presented. From 973 total conotruncal cases, a burden of rare CNVs was detected in both cohorts. Candidate genes from rare CNVs found in both cohorts were identified based on their association with cardiac development or disease, and/or their reported disruption in published studies. Functional and pathway analyses revealed significant enrichment of terms involved in either heart or early embryonic development.

CONCLUSION

Our study tested one of the largest cohorts specifically with cardiac conotruncal and related defects. These results confirm and extend previous findings that CNVs contribute to disease risk for congenital heart defects in general and conotruncal defects in particular. As disease heterogeneity renders identification of single recurrent genes or loci difficult, functional pathway and gene regulation network analyses appear to be more informative. Birth Defects Research 109:271-295, 2017. © 2017 Wiley Periodicals, Inc.

Transcriptomic dynamics of breast cancer progression in the MMTV-PyMT mouse model

BACKGROUND

Malignant breast cancer with complex molecular mechanisms of progression and metastasis remains a leading cause of death in women. To improve diagnosis and drug development, it is critical to identify panels of genes and molecular pathways involved in tumor progression and malignant transition. Using the PyMT mouse, a genetically engineered mouse model that has been widely used to study human breast cancer, we profiled and analyzed gene expression from four distinct stages of tumor progression (hyperplasia, adenoma/MIN, early carcinoma and late carcinoma) during which malignant transition occurs.

RESULTS

We found remarkable expression similarity among the four stages, meaning genes altered in the later stages showed trace in the beginning of tumor progression. We identified a large number of differentially expressed genes in PyMT samples of all stages compared with normal mammary glands, enriched in cancer-related pathways. Using co-expression networks, we found panels of genes as signature modules with some hub genes that predict metastatic risk. Time-course analysis revealed genes with expression transition when shifting to malignant stages. These may provide additional insight into the molecular mechanisms beyond pathways.

CONCLUSIONS

Thus, in this study, our various analyses with the PyMT mouse model shed new light on transcriptomic dynamics during breast cancer malignant progression.

A familial study of azoospermic men identifies three novel causative mutations in three new human azoospermia genes

PURPOSE

Up to 1% of all men experience azoospermia, a condition of complete absence of sperm in the semen. The mechanisms and genes involved in spermatogenesis are mainly studied in model organisms, and their relevance to humans is unclear because human genetic studies are very scarce. Our objective was to uncover novel human mutations and genes causing azoospermia due to testicular meiotic maturation arrest.

METHODS

Affected and unaffected siblings from three families were subjected to whole-exome or whole-genome sequencing, followed by comprehensive bioinformatics analyses to identify mutations suspected to cause azoospermia. These likely mutations were further screened in azoospermic and normozoospermic men and in men proven to be fertile, as well as in a reference database of local populations.

RESULTS

We identified three novel likely causative mutations of azoospermia in three genes: MEIOB, TEX14, and DNAH6. These genes are associated with different meiotic processes: meiotic crossovers, daughter cell abscission, and possibly rapid prophase movements.

CONCLUSION

The genes and pathways we identified are fundamental for delineating common causes of azoospermia originating in mutations affecting diverse meiotic processes and have great potential for accelerating approaches to diagnose, treat, and prevent infertility.Genet Med advance online publication 16 February 2017.

SMCHD1 mutations associated with a rare muscular dystrophy can also cause isolated arhinia and Bosma arhinia microphthalmia syndrome

Arhinia, or absence of the nose, is a rare malformation of unknown etiology that is often accompanied by ocular and reproductive defects. Sequencing of 40 people with arhinia revealed that 84% of probands harbor a missense mutation localized to a constrained region of SMCHD1 encompassing the ATPase domain. SMCHD1 mutations cause facioscapulohumeral muscular dystrophy type 2 (FSHD2) via a trans-acting loss-of-function epigenetic mechanism. We discovered shared mutations and comparable DNA hypomethylation patterning between these distinct disorders. CRISPR/Cas9-mediated alteration of smchd1 in zebrafish yielded arhinia-relevant phenotypes. Transcriptome and protein analyses in arhinia probands and controls showed no differences in SMCHD1 mRNA or protein abundance but revealed regulatory changes in genes and pathways associated with craniofacial patterning. Mutations in SMCHD1 thus contribute to distinct phenotypic spectra, from craniofacial malformation and reproductive disorders to muscular dystrophy, which we speculate to be consistent with oligogenic mechanisms resulting in pleiotropic outcomes.

An Asymmetrically Balanced Organization of Kinases versus Phosphatases across Eukaryotes Determines Their Distinct Impacts

Protein phosphorylation underlies cellular response pathways across eukaryotes and is governed by the opposing actions of phosphorylating kinases and de-phosphorylating phosphatases. While kinases and phosphatases have been extensively studied, their organization and the mechanisms by which they balance each other are not well understood. To address these questions we performed quantitative analyses of large-scale 'omics' datasets from yeast, fly, plant, mouse and human. We uncovered an asymmetric balance of a previously-hidden scale: Each organism contained many different kinase genes, and these were balanced by a small set of highly abundant phosphatase proteins. Kinases were much more responsive to perturbations at the gene and protein levels. In addition, kinases had diverse scales of phenotypic impact when manipulated. Phosphatases, in contrast, were stable, highly robust and flatly organized, with rather uniform impact downstream. We validated aspects of this organization experimentally in nematode, and supported additional aspects by theoretic analysis of the dynamics of protein phosphorylation. Our analyses explain the empirical bias in the protein phosphorylation field toward characterization and therapeutic targeting of kinases at the expense of phosphatases. We show quantitatively and broadly that this is not only a historical bias, but stems from wide-ranging differences in their organization and impact. The asymmetric balance between these opposing regulators of protein phosphorylation is also common to opposing regulators of two other post-translational modification systems, suggesting its fundamental value.

Protein phosphorylation is a reversible modification that underlies cellular responses to stimuli across organisms. Historically, the study of protein phosphorylation concentrated on the role of kinases, which introduce the phosphate, at the expense of phosphatases, which remove it. Many kinases have been associated with specific phenotypes and considered attractive drug targets, while phosphatases remained far less characterized. It has been unclear whether this discrepancy is due to historical biases or reflects real systemic differences between these enzymes. By analyzing large-scale ‘omics’ datasets across genes, transcripts, proteins, interactions, and organisms, we uncovered an asymmetric architecture of kinases versus phosphatases that balances between them, determines their distinct impact patterns, and affects their therapeutic potential. This architecture is conserved from yeast to human and is partially shared by two other protein modification systems, suggesting it is a general feature of these systems.